Article Instance

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{
    "pk": 25682,
    "title": "Upsetting the contingency table: Causal induction over sequences of point events",
    "subtitle": null,
    "abstract": "Data continuously stream into our minds, guiding our learn-\ning and inference with no trial delimiters to parse our experi-\nence. These data can take on a variety of forms, but research\non causal learning has emphasized discrete contingency data\nover continuous sequences of events. We present a formal\nframework for modeling causal inferences about sequences\nof point events, based on Bayesian inference over nonhomo-\ngeneous Poisson processes (NHPPs). We show how to apply\nthis framework to successfully model data from an experiment\nby Lagnado and Speekenbrink (2010) which examined human\nlearning from sequences of point events.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "causal inference; continuous time; stochastic pro-\ncesses; Bayesian models"
        }
    ],
    "section": "Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/0cr5s8z1",
    "frozenauthors": [
        {
            "first_name": "Michael",
            "middle_name": "D",
            "last_name": "Pacer",
            "name_suffix": "",
            "institution": "UCB",
            "department": ""
        },
        {
            "first_name": "Thomas",
            "middle_name": "L",
            "last_name": "Griffiths",
            "name_suffix": "",
            "institution": "UCB",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2015-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/25682/galley/15306/download/"
        }
    ]
}